diff --git a/Models/model_host.py b/Models/model_host.py
index 6700045..9febfec 100644
--- a/Models/model_host.py
+++ b/Models/model_host.py
@@ -107,22 +107,22 @@ def predict_resnet50_20241024_270():
     return response
 
 # This version expects the image to be of the form (x,x,3).  
-@app.route('/predict_resnet50B', methods=['GET','POST'])
-def predict_resnet50B():
-    print('/predict_resnet50B')
-    test_image=request.get_data()
-    save_image(test_image)
-    test_image = PIL.Image.open(io.BytesIO(test_image))
-    test_array=keras.preprocessing.image.img_to_array(test_image)
-    batch_test_array=np.array([test_array])
-    predictions=resnet50b_model.predict(batch_test_array)
-    if type(predictions) == list:
-        average_prediction = sum(predictions)/len(predictions)
-        threshold_output = np.where(average_prediction > 0.5, 1, 0)
-    else :
-        threshold_output = np.where(predictions > 0.5, 1, 0)
-    response=str(predictions)+'-->'+str(threshold_output)
-    return response
+# @app.route('/predict_resnet50B', methods=['GET','POST'])
+# def predict_resnet50B():
+#     print('/predict_resnet50B')
+#     test_image=request.get_data()
+#     save_image(test_image)
+#     test_image = PIL.Image.open(io.BytesIO(test_image))
+#     test_array=keras.preprocessing.image.img_to_array(test_image)
+#     batch_test_array=np.array([test_array])
+#     predictions=resnet50b_model.predict(batch_test_array)
+#     if type(predictions) == list:
+#         average_prediction = sum(predictions)/len(predictions)
+#         threshold_output = np.where(average_prediction > 0.5, 1, 0)
+#     else :
+#         threshold_output = np.where(predictions > 0.5, 1, 0)
+#     response=str(predictions)+'-->'+str(threshold_output)
+#     return response
 
 @app.route('/predict_lenet5', methods=['GET','POST'])
 def predict_lenet5():
@@ -156,23 +156,28 @@ def process_image():
 
 
 if __name__ == '__main__':
-    resnet50_model_name='../Weights/resnet50.h5'
+    path = os.getcwd()
+    resnet50_model_name='Weights/resnet50.h5.keras'
+    resnet50_model_name = os.path.realpath(path + '/' +resnet50_model_name)
     print('Loading {model_name}'.format(model_name=resnet50_model_name))
     resnet50_model = keras.models.load_model(resnet50_model_name)  
 
-    resnet50_20241024_270_model_name='../Weights/resnet50_20241024_270.h5.keras'
+    resnet50_20241024_270_model_name='Weights/resnet50_20241024_270.h5.keras'
+    resnet50_20241024_270_model_name = os.path.realpath(path + '/' + resnet50_20241024_270_model_name)
     print('Loading {model_name}'.format(model_name=resnet50_20241024_270_model_name))
     resnet50_20241024_270_model = keras.models.load_model(resnet50_20241024_270_model_name)
 
-    resnet50b_model_name='../Weights/resnet50B.h5'
-    print('Loading {model_name}'.format(model_name=resnet50b_model_name))
-    resnet50b_model = keras.models.load_model(resnet50b_model_name)     
+    # resnet50b_model_name='../Weights/resnet50B.h5'
+    # print('Loading {model_name}'.format(model_name=resnet50b_model_name))
+    # resnet50b_model = keras.models.load_model(resnet50b_model_name)     
 
-    vgg16_model_name='../Weights/vggnet16.h5'
+    vgg16_model_name='Weights/vggnet16.h5.keras'
+    vgg16_model_name = os.path.realpath(path + '/' + vgg16_model_name)
     print('Loading {model_name}'.format(model_name=vgg16_model_name))    
     vgg16_model=keras.models.load_model(vgg16_model_name) 
 
-    lenet_model_name='../Weights/lenet5.h5'
+    lenet_model_name='Weights/lenet5.h5.keras'
+    lenet_model_name = os.path.realpath(path + '/' +lenet_model_name)
     print('Loading {model_name}'.format(model_name=lenet_model_name))
     lenet_model=keras.models.load_model(lenet_model_name)    
 
diff --git a/Models/model_sk_resnet.py b/Models/model_sk_resnet.py
index 8100242..27bde81 100644
--- a/Models/model_sk_resnet.py
+++ b/Models/model_sk_resnet.py
@@ -21,7 +21,7 @@ tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
 image_size = (128, 128)
 
 #model_weights_file='model_resnet50.hdf5'
-model_name='resnet50_20241022.h5.keras'
+model_name='resnet50.h5.keras'
 
 train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
     'C:\\boneyard\\DeepLearning\\data',
diff --git a/Models/model_sk_vgg16.py b/Models/model_sk_vgg16.py
index cdec16e..d03efab 100644
--- a/Models/model_sk_vgg16.py
+++ b/Models/model_sk_vgg16.py
@@ -25,8 +25,7 @@ from time import time
 
 image_size = (128, 128)
 
-#model_name='vgg16'
-model_weights_file='model_vgg16.h5.keras'
+model_weights_file='vggnet16.h5.keras'
 
 train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
     'C:\\boneyard\\DeepLearning\\data',
@@ -171,7 +170,7 @@ model.compile(loss='binary_crossentropy', optimizer=optimizer,metrics=['accuracy
 # The callback to checkpointer saves the model wights.  Other callback can be added...like a stopping function
 # history=model.fit_generator(dataGen.flow(x_train,y_train,batch_size=batch_size),callbacks=[checkpointer],steps_per_epoch=x_train.shape[0] //
 # batch_size,epochs=epochs,verbose=2,validation_data=(x_valid,y_valid))
-history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[earlystopping,tensorboard,checkpointer])
+history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[early_stopping,tensorboard,checkpointer])
 
 # Here is the evaluation part
 # score=model.evaluate(x=train_ds,verbose=1)